Visual working memory (WM) is a central cognitive system for maintaining active representations about objects in the environment so that they may be manipulated or acted upon. Individual differences in WM ability in healthy populations appear to reflect a core cognitive ability because they strongly predict an individual's fluid intelligenc as well as several aspects of scholastic achievement. Furthermore, WM deficits are a signature of many prevalent mental health disorders. Thus, a detailed understanding of this system is essential if we are to understand and treat psychopathologies that involve impaired cognition such as attention deficit/ hyperactivity disorder (ADHD) or schizophrenia. In addition, because of the tight link between online visual memory and basic processes for visual perception, our work will help to integrate basic knowledge about visual sensory processing (e.g., population coding of simple visual features) and online memory. One of the most fundamental attributes of WM is that it is greatly limited in capacity: capable of storing information about just a few objects at time, each with a limited level of precision. A key recent discovery is that these number and precision limits are distinct facets of WM capacity. However, the neural mechanisms that underlie these two factors that determine capacity are not currently understood. Here, we are developing neural oscillatory and hemodynamic measures that enable tracking of both between- and within-subject variations in these abilities. Specifically, our preliminary data show that the number of items held in WM is indexed on a trial-by-trial basis by desynchronization in alpha power (8-12hz) and WM precision is indexed by the dispersion of sensory population codes (quantified via novel multivariate analyses of fMRI and EEG data). The proposed research will employ psychophysics, fMRI, and EEG to measure the neural signals that track number and precision in WM. These efforts will provide new insights into the functional subdivisions of visual WM, and build clear bridges between well characterized behavioral measures of online memory ability and measures of oscillatory activity in humans. Finally, we will also examine the interactions between visual WM and visual long term memory (LTM) to determine how the contents of WM determine that which is encoded into LTM. These studies will help to characterize the role of visual WM in associative learning and clarify the roles of each system in the guidance of complex behaviors. By more precisely understanding how healthy individuals differ in WM ability and visual sensory function, we hope to develop methods and procedures that can be used to more accurately detect and characterize disease states in populations with mental health disorders, and to diagnose and quantify disorders in visually-guided behavior.